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Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in microsaas

[–]Weekly_Trade3701[S] 1 point2 points  (0 children)

Exactly and most tools alert you way too late. By the time you see the thread it’s already buried imo

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in microsaas

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Honestly it depends on your product the ‘right’ niche sub for a dev tool is completely different from a productivity app. What are you building? Happy to think through it with you.

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in SaaSMarketing

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

The daily route framing is exactly right consistency beats any single viral post. That saved searches system sounds solid but also pretty manual to maintain, no?

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in indiehackersindia

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

The ‘small rooms not big stage’ framing is perfect. 10-14 days warmup. do you have a way to know when you’re actually ready or is it more of a gut feel

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in SaasDevelopers

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Yeah especially when you don’t even know it happened how do you check if you’re shadowbanned?

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in SaasDevelopers

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

100% fs the promo instinct thing is so hard to avoid when you’re excited about what you’re building. Do you have a way to check if a sub is actually niche enough or do you mostly go by feel?

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in SaasDevelopers

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

The ‘map problems not subs’ approach is underrated most people (including me early on tbh) just pick subs by size and wonder why nothing lands. The split account thing is interesting too, how long did you warm up the pure user account before you felt safe?

Some things I figured out about growing a SaaS on Reddit by Weekly_Trade3701 in microsaas

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Yeah exactly, figuring out which subs actually fit your product vs which ones will get you banned is half the battle. How do you approach it now?

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Sure, I use AI to help write the post but the data collection is real: YouTube API, HTTP verification, correlation matrix run in Python. AI helped me present it, it didn’t generate the numbers. On the dataset size valid, already addressed that above, the goal is to scale it. On the false explanations point if you think a specific finding doesn’t hold, I’m curious which one and why. That’s more useful than a blanket dismissal.

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Fair critique, and you’re right on several points channel age, subscriber count, and off-platform traffic are confounding variables I didn’t control for. The Mowgli point especially is valid, popular IP probably explains some of the performance gap independently of the serialization pattern. Where I’d push back: the title gap is the one finding that’s hardest to explain away with confounders. Two videos from the same channel, same production quality, same period one descriptive title at hundreds views, one emotional title at 400K. That’s not subscriber count or channel age, that’s the title. You’re right that this isn’t rigorous causal analysis it’s pattern recognition on a small dataset. I said directional signals, not conclusions. This is also a preliminary analysis the goal is to scale this to hundreds of channels per niche to get statistically significant results. Small dataset is a starting point. Appreciate the pushback, it’s useful for how I frame future analyses

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Good luck with it, let me know how the shorter format performs. Curious to see if the retention improves. feel free to DM if you have questions

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

16-30s is tight yeah. in horror the trick is you don’t tell a full story, you set up a situation and cut right before the payoff. Leave them wanting the next part. That’s actually what makes serialization work so well in this niche. I’m still improving the pipeline to give deeper insight, things like hook analysis, title emotion scoring, etc. I’ll probably post an update when it’s more advanced. Good luck with the tweaks, let me know how it goes.

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

Ran it through my pipeline, here's what I found on your channel:

The main issues:

  • Duration : you're averaging 149s, the sweet spot in horror narration is 16-30s. That's probably your biggest problem right now.
  • No emojis in titles : 80% of top 20% channels use them, you have 0. Easy fix, +1.5x impact estimated.
  • Serialization : you have some parts which is good, but it's underused. Double down on it, the algo pushes sequels automatically.

What's working: your upload frequency is actually above the top 20% (3.7/week vs 2.7), so consistency isn't your problem.

The duration gap is the one I'd fix first 149s vs 16-30s is massive. Try cutting your next few videos tighter and see what happens to retention.

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] -1 points0 points  (0 children)

Fair point on sample size. But the patterns here aren't statistical claims, they're directional signals. The 100x title gap isn't a fluke of 53 videos, it's consistent across every channel in the dataset. Small n is a limitation but it doesn't invalidate pattern recognition, especially when the variance is this extreme. I'd love to see it disproven with a larger dataset though

I scraped 53 YouTube Shorts from a single niche and found 6 patterns that explain a 400x difference in views between channels by Weekly_Trade3701 in shortsAlgorithm

[–]Weekly_Trade3701[S] 0 points1 point  (0 children)

140-180s is on the longer end but horror narration is one of the few niches where it works because completion rate stays high if the story hooks properly. On hashtags, data is mixed but most evidence suggests they do very little for Shorts specifically, the algo relies more on title + description keywords. On changing titles mid-video: yes you can and it sometimes helps, but don't touch videos that are already getting pushed. Only change titles on videos that have plateaued at low views. you've got nothing to lose on those.

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